import os

import torch

from options.predict_options import PredictOption
from data import create_dataset
from models import create_model
from util.visualizer import save_images
from util import html
import wandb

if __name__ == '__main__':
    opt = PredictOption().parse()  # get test options
    # hard-code some parameters for test
    opt.num_threads = 0
    opt.batch_size = 1
    opt.serial_batches = True
    opt.no_flip = True
    opt.display_id = -1
    opt.use_wandb = True
    # 预测修改这个函数
    dataset = create_dataset(opt)
    model = create_model(opt)
    model.setup(opt)

    # initialize logger
    if opt.use_wandb:
        wandb_run = wandb.init(project='CycleGAN-and-pix2pix', name=opt.name, config=opt) if not wandb.run else wandb.run
        wandb_run._label(repo='CycleGAN-and-pix2pix')

    # create a website
    web_dir = os.path.join(opt.results_dir, opt.name, '{}_{}'.format(opt.phase, opt.epoch))  # define the website directory
    if opt.load_iter > 0:  # load_iter is 0 by default
        web_dir = '{:s}_iter{:d}'.format(web_dir, opt.load_iter)
    print('creating web directory', web_dir)
    webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.epoch))

    if opt.eval:
        model.eval()

    with torch.no_grad():
        for i, data in enumerate(dataset):
            if i >= opt.num_test:  # only apply our model to opt.num_test images.
                break
            model.set_pre_input(data)  # unpack data from data loader
            model.test()           # run inference
            visuals = model.get_pre_current_visuals()  # get image results
            img_path = model.get_image_paths()     # get image paths

            if i % 5 == 0:  # save images to an HTML file
                print('processing (%04d)-th image... %s' % (i, img_path))
            save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio, width=opt.display_winsize, use_wandb=opt.use_wandb)
        webpage.save()  # save the HTML
